I’ve just spoken on “A starter data science process for software engineers” (slides linked) at PyLondinium 2019, this talk is aimed at software engineers who are starting to ask data related questions and who are starting a data science journey. I’ve noted that many software engineers – without a formal data science background – are joining our PyData/data science world but lack useful transitionary resources. [note – video to come]
- What enables a good data science project
- Ways to plan a project spec for success (really, do this, it saves so much pain)
- A live demo covering a Jupyter Notebook with Altair, matplotlib, sklearn, yellowbrick, Widgets and then serve this up with Voila and Binder
The Notebook lives in github and this link should start a live Binder version (in which Altair is interactive and the slider Widget at the bottom of the Notebook live-drives scikit-learn predictions).
After the talk it seems that both Altair and the message “make a project spec” were the main winners, with Voila as a close third.
PyLondinium were also kind enough to organise a book signing for my High Performance Python book where I got to talk a bit about our in-preparation 2nd edition (for January).
This conference builds on last year’s inaugural event, it has grown and has a lovely feel. You may want to think on putting in a talk for next year’s PyLondinium!
Ian is a Chief Interim Data Scientist via his Mor Consulting. Sign-up for Data Science tutorials in London and to hear about his data science thoughts and jobs. He lives in London, is walked by his high energy Springer Spaniel and is a consumer of fine coffees.